In the related art, there is a technique to identify an object by comparing a feature value (feature data) extracted from an image of the object with a feature value of each of a plurality of registered objects. Such a technique is called as generic object recognition. The generic object recognition can be typically applied to a store system such as a point-of-sale (POS) terminal, to identify a product to be purchased that does not have a symbol code thereon, such as vegetables, fruits, and so on.
Accuracy of the object recognition basically depends on similarity of the feature value of the registered objects with the feature value of the object to be identified. To maintain the accuracy, a new feature value that is likely to have high similarity with the feature value of the object to be identified may need to be added to a storage unit that stored the feature values of the registered objects. When there is not sufficient space in the storage unit, one of the stored feature values may need to be replaced with the new feature data. When a plurality of feature values is registered for a registered product, it would be preferable to select one of the feature values for replacement, so as not to reduce the accuracy.